Correcting the Bias of Empirical Frequency Parameter Estimators in Codon Models

Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a “corrected” empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators

Coriolis force in Geophysics: an elementary introduction and examples

We show how Geophysics may illustrate and thus improve classical Mechanics lectures concerning the study of Coriolis force effects. We are then interested in atmospheric as well as oceanic phenomena we are familiar with, and are for that reason of pedagogical and practical interest. Our aim is to model them in a very simple way to bring out the physical phenomena that are involved.Comment: Accepted for publication in European Journal of Physic

Generalized Rosenfeld scalings for tracer diffusivities in not-so-simple fluids: Mixtures and soft particles

Rosenfeld [Phys. Rev. A 15, 2545 (1977)] noticed that casting transport coefficients of simple monatomic, equilibrium fluids in specific dimensionless forms makes them approximately single-valued functions of excess entropy. This has predictive value because, while the transport coefficients of dense fluids are difficult to estimate from first principles, excess entropy can often be accurately predicted from liquid-state theory. Here, we use molecular simulations to investigate whether Rosenfeld's observation is a special case of a more general scaling law relating mobility of particles in mixtures to excess entropy. Specifically, we study tracer diffusivities, static structure, and thermodynamic properties of a variety of one- and two-component model fluid systems with either additive or non-additive interactions of the hard-sphere or Gaussian-core form. The results of the simulations demonstrate that the effects of mixture concentration and composition, particle-size asymmetry and additivity, and strength of the interparticle interactions in these fluids are consistent with an empirical scaling law relating the excess entropy to a new dimensionless (generalized Rosenfeld) form of tracer diffusivity, which we introduce here. The dimensionless form of the tracer diffusivity follows from knowledge of the intermolecular potential and the transport / thermodynamic behavior of fluids in the dilute limit. The generalized Rosenfeld scaling requires less information, and provides more accurate predictions, than either Enskog theory or scalings based on the pair-correlation contribution to the excess entropy. As we show, however, it also suffers from some limitations, especially for systems that exhibit significant decoupling of individual component tracer diffusivities.Comment: 15 pages, 10 figure

CodonTest: Modeling Amino Acid Substitution Preferences in Coding Sequences

Codon models of evolution have facilitated the interpretation of selective forces operating on genomes. These models, however, assume a single rate of non-synonymous substitution irrespective of the nature of amino acids being exchanged. Recent developments have shown that models which allow for amino acid pairs to have independent rates of substitution offer improved fit over single rate models. However, these approaches have been limited by the necessity for large alignments in their estimation. An alternative approach is to assume that substitution rates between amino acid pairs can be subdivided into rate classes, dependent on the information content of the alignment. However, given the combinatorially large number of such models, an efficient model search strategy is needed. Here we develop a Genetic Algorithm (GA) method for the estimation of such models. A GA is used to assign amino acid substitution pairs to a series of rate classes, where is estimated from the alignment. Other parameters of the phylogenetic Markov model, including substitution rates, character frequencies and branch lengths are estimated using standard maximum likelihood optimization procedures. We apply the GA to empirical alignments and show improved model fit over existing models of codon evolution. Our results suggest that current models are poor approximations of protein evolution and thus gene and organism specific multi-rate models that incorporate amino acid substitution biases are preferred. We further anticipate that the clustering of amino acid substitution rates into classes will be biologically informative, such that genes with similar functions exhibit similar clustering, and hence this clustering will be useful for the evolutionary fingerprinting of genes

Modeling HIV-1 Drug Resistance as Episodic Directional Selection

The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance

Irreducible Representations of Diperiodic Groups

The irreducible representations of all of the 80 diperiodic groups, being the symmetries of the systems translationally periodical in two directions, are calculated. To this end, each of these groups is factorized as the product of a generalized translational group and an axial point group. The results are presented in the form of the tables, containing the matrices of the irreducible representations of the generators of the groups. General properties and some physical applications (degeneracy and topology of the energy bands, selection rules, etc.) are discussed.Comment: 30 pages, 5 figures, 28 tables, 18 refs, LaTex2.0

Loss of the Intellectual Disability and Autism Gene Cc2d1a and Its Homolog Cc2d1b Differentially Affect Spatial Memory, Anxiety, and Hyperactivity

Hundreds of genes are mutated in non-syndromic intellectual disability (ID) and autism spectrum disorder (ASD), with each gene often involved in only a handful of cases. Such heterogeneity can be daunting, but rare recessive loss of function (LOF) mutations can be a good starting point to provide insight into the mechanisms of neurodevelopmental disease. Biallelic LOF mutations in the signaling scaffold CC2D1Acause a rare form of autosomal recessive ID, sometimes associated with ASD and seizures. In parallel, we recently reported that Cc2d1a-deficient mice present with cognitive and social deficits, hyperactivity and anxiety. In Drosophila, loss of the only ortholog of Cc2d1a, lgd, is embryonically lethal, while in vertebrates, Cc2d1a has a homolog Cc2d1b which appears to be compensating, indicating that Cc2d1a and Cc2d1b have a redundant function in humans and mice. Here, we generate an allelic series of Cc2d1a and Cc2d1b LOF to determine the relative role of these genes during behavioral development. We generated Cc2d1b knockout (KO), Cc2d1a/1b double heterozygous and double KO mice, then performed behavioral studies to analyze learning and memory, social interactions, anxiety, and hyperactivity. We found that Cc2d1a and Cc2d1b have partially overlapping roles. Overall, loss of Cc2d1b is less severe than loss of Cc2d1a, only leading to cognitive deficits, while Cc2d1a/1b double heterozygous animals are similar to Cc2d1a-deficient mice. These results will help us better understand the deficits in individuals with CC2D1A mutations, suggesting that recessive CC2D1B mutations and trans-heterozygous CC2D1A and CC2D1B mutations could also contribute to the genetics of ID

HIV-Specific Probabilistic Models of Protein Evolution

Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1) genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1–the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic error. We argue that our model derivation procedure is immediately applicable to other organisms with extensive sequence data available, such as Hepatitis C and Influenza A viruses

Australian long-term care personnel's knowledge and attitudes regarding palliative care for people with advanced dementia.

This study aimed to describe Australian long-term care (LTC) personnel's knowledge and attitudes concerning palliative care for residents with advanced dementia, and explore relationships with LTC facility/personnel characteristics. An analysis was undertaken of baseline data from a cluster randomised controlled trial of facilitated family case conferencing for improving palliative care of LTC residents with advanced dementia (the 'IDEAL Study'). Participants included any LTC personnel directly involved in residents' care. Knowledge and attitudes concerning palliative care for people with advanced dementia were measured using the questionnaire on Palliative Care for Advanced Dementia. Univariate and multivariate analyses explored relationships between personnel knowledge/attitudes and facility/personnel characteristics. Of 307 personnel in the IDEAL Study, 290 (94.5%) from 19/20 LTCFs provided sufficient data for inclusion. Participants included 9 (2.8%) nurse managers, 59 (20.5%) registered nurses, 25 (8.7%) enrolled nurses, 187 (64.9%) assistants in nursing/personal care assistants and 9 (3.1%) care service employees. In multivariate analyses, a facility policy not to rotate personnel through dementia units was the only variable associated with more favourable overall personnel knowledge and attitudes. Other variables associated with favourable knowledge were a designation of nursing manager or registered or enrolled nurse, and having a preferred language of English. Other variables associated with favourable attitudes were tertiary level of education and greater experience in dementia care. Like previous international research, this study found Australian LTC personnel knowledge and attitudes regarding palliative care for people with advanced dementia to be associated with both facility and personnel characteristics. Future longitudinal research is needed to better understand the relationships between knowledge and attitudes, as well as between these attributes and quality of care